60 citations · 97 across the 12 of their papers we have counts for
8 papers · 1 filter
Achieve Optimal Adversarial Accuracy for Adversarial Deep Learning using Stackelberg Game
Xiao-Shan Gao, Shuang Liu, Lijia Yu
Adversarial deep learning is to train robust DNNs against adversarial attacks, which is one of the major research focuses of deep learning. Game theory has been used to answer some…
Improving Policy Optimization with Generalist-Specialist Learning
Zhiwei Jia, Xuanlin Li, Zhan Ling +3
Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations. We empirically ob…
Provably Efficient Kernelized Q-Learning
Shuang Liu, Hao Su
We propose and analyze a kernelized version of Q-learning. Although a kernel space is typically infinite-dimensional, extensive study has shown that generalization is only affected…
Regret Bounds for Discounted MDPs
Shuang Liu, Hao Su
Reinforcement learning (RL) has traditionally been understood from an episodic perspective; the concept of non-episodic RL, where there is no restart and therefore no reliable reco…
Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li, Quan Vuong, Shuang Liu +5
We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks…
The Inductive Bias of Restricted f-GANs
Shuang Liu, Kamalika Chaudhuri
Generative adversarial networks are a novel method for statistical inference that have achieved much empirical success; however, the factors contributing to this success remain ill…
Approximation and Convergence Properties of Generative Adversarial Learning
Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
Generative adversarial networks (GAN) approximate a target data distribution by jointly optimizing an objective function through a "two-player game" between a generator and a discr…
Regret vs. Communication: Distributed Stochastic Multi-Armed Bandits and Beyond
Shuang Liu, Cheng Chen, Zhihua Zhang
In this paper, we consider the distributed stochastic multi-armed bandit problem, where a global arm set can be accessed by multiple players independently. The players are allowed…